Summary of Buzz to Broadcast: Predicting Sports Viewership Using Social Media Engagement, by Anakin Trotter
Buzz to Broadcast: Predicting Sports Viewership Using Social Media Engagement
by Anakin Trotter
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A regression-based approach is proposed to predict sports viewership using social media metrics from platforms like Reddit. The method incorporates post counts, comments, scores, and sentiment analysis from TextBlob and VADER. Iterative improvements are made by focusing on major sports subreddits, incorporating categorical features, and handling outliers by sport. The model achieves an R^2 of 0.99, a Mean Absolute Error (MAE) of 1.27 million viewers, and a Root Mean Squared Error (RMSE) of 2.33 million viewers on the full dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sports viewership is hard to predict, but this study shows how social media can help. By looking at posts, comments, scores, and how people feel about sports (positive or negative), we can make a pretty good guess about who will watch what games. The method gets even better by focusing on big sports communities online and making sure it handles unusual data points well. This could be super useful for companies to figure out how much money they’ll make from ads before events, and where to target those ads. |
Keywords
» Artificial intelligence » Mae » Regression
